Spatial Statistics 2021: Climate and the Environment

Country: USA

City: Boulder

Abstr. due: 15.01.2021

Dates: 20.07.21 — 23.07.21

Area Of Sciences: Geography;

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Organizing comittee e-mail: https://service.elsevier.com/app/contact/supporthub/conferences/RN.Incident.CustomFields.conference_

Organizers: Elsevier / University of Colorado

 

The conference will provide a forum to debate and discuss how to use spatially referenced data to advance our understanding and provide support for decision making in the domain of Earth system dynamics.

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Our physical environment is dynamic, continuously evolving at many scales of time and space. Understanding the Earth’s climate system has become even more critical in recent decades with the realization that many parts of society and ecological systems are vulnerable to rapid change. The mechanisms for these changes need to be better understood because of the great consequences they have, for society and the environment well into this century and beyond.

Climate is the result of many diverse processes such as: local rainfall and temperature, land use and vegetation, or the global jetstream and ocean currents.  Weather that historically would be considered extreme is now more common, and the vulnerability of economies and infrastructure, particularly for developing countries, to large weather events make seasonal forecasting critical.  Effects on climate can come from variations in the Sun’s radiation, to human activities in transport and industry, deforestation and urban concentrations. The effects can be diverse: different patterns may emerge in epidemics, stresses can develop on local ecosystems, or sea levels can rise for coastal areas. These impacts have complex dynamics and feedbacks, have many uncertain components, and so require solid, statistically sound predictions for a wide variety of stakeholders. The field of spatial statistics has developed in recent years to address many of these challenging problems connected to the Earth system. This includes increasing attention on deep learning methods, applications of Bayesian methodology for large data volumes, extreme value theory, and the synthesis of spatial and temporal models for representing climate processes. The need for well grounded spatial and spatio-temporal statistics is huge, being the leading discipline to interpret observational data and also attach measures of uncertainty to conclusions and predictions.

This conference will focus on climate change dynamics, their causes, their effects and their future. The conference theme will be the perspective of the Earth as a unified system with connections and feedbacks between physical and biological spheres and also human activities.

Crucial developments in the methodology are in new scalable methods, spatio-temporal statistics, prediction and statistical aspects of modeling, like spatial and spatio-temporal extremes, attribution and forecasting.

Methods

  • Space-time statistics, e.g. geostatistics, point patterns, estimation methods, large dimensions
  • Spatial deep learning
  • Inverse modeling
  • Modeling of extremes
  • Stochastic geometry, tesselation, point processes, random sets
  • Causal statistical modeling
  • Trajectory/movement modeling

Applications

  • Climate system modeling and observations
  • Health e.g. epidemiology, geohealth and global health
  • Spatially-Explicit Ecological Models
  • Plant and animal epidemiology
  • Quantifying the spatial extent of hazards and risk
  • Crime and poverty mapping
  • Space/time econometrics
  • Interface of Neural Computing and Spatial/Spatio-Temporal Statistics
  • Inferring Movement and Behavior from Telemetry

Conference Web-Site: https://www.elsevier.com/events/conferences/spatial-statistics